Introduction to Bayesian Statistics Using R

About

Basics of Bayesian Data Analysis Using R is part one of the Bayesian Data Analysis in R professional certificate.

Bayesian approach is becoming increasingly popular in all fields of data analysis, including but not limited to epidemiology, ecology, economics, and political sciences. It also plays an increasingly important role in data mining and deep learning. Let this course be your first step into Bayesian statistics.

Here, you will find a practical introduction to applied Bayesian data analysis with the emphasis on formulating and answering real life questions. You will learn how to combine the data generating mechanism, likelihood, with prior distribution using Bayes' Theorem to produce the posterior distribution. You will investigate the underlying theory and fundamental concepts by way of simple and clear practical examples, including a case of linear regression.

You will be introduced to the Gibbs sampler " the simplest version of the powerful Markov Chain Monte Carlo (MCMC) algorithm. And you will see how the popular R-software can be used in this context, and encounter some Bayesian R packages .

A facility in basic algebra and calculus as well as programming in R is recommended.

What you will learn

• Bayes’ Theorem. Differences between classical (frequentist) and Bayesian inference.

• Posterior inference: summarizing posterior distributions, credible intervals, posterior probabilities, posterior predictive distributions and data visualisation.

• Gamma-poisson, beta-binomial and normal conjugate models for data analysis.

• Bayesian regression analysis and analysis of variance (ANOVA).

• Use of simulations for posterior inference. Simple applications of Markov chain-Monte Carlo (MCMC) methods and their implementation in R.

• Bayesian cluster analysis.

• Model diagnostics and comparison.

• Ensuring you answer the actual research question rather than “apply methods to the data”

Skills you learn

• Data Mining
• Epidemiology
• R (Programming Language)
• Elementary Algebra
• Deep Learning
• Markov Chain Monte Carlo
• Economics
• Political Sciences
• Calculus
• Data Analysis
• Ecology
• Bayesian Statistics

Auto Summary

Introduction to Bayesian Statistics Using R is an engaging foundational course offered by edX, ideal for those in IT & Computer Science. Spanning 14 weeks, this professional subscription course delves into Bayesian data analysis, emphasizing real-life applications. Learners will explore Bayes' Theorem, Gibbs sampler, linear regression, and practical R programming. Basic algebra, calculus, and R knowledge are recommended. Perfect for beginners aiming to gain practical Bayesian analysis skills.